{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-01T13:04:54.582596Z",
     "iopub.status.busy": "2021-06-01T13:04:54.581591Z",
     "iopub.status.idle": "2021-06-01T13:04:54.598596Z",
     "shell.execute_reply": "2021-06-01T13:04:54.596595Z",
     "shell.execute_reply.started": "2021-06-01T13:04:54.582596Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as ss\n",
    "from pandas.tseries.offsets import Hour,Minute,Day\n",
    "from datetime import datetime\n",
    "from dateutil.parser import parse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-01T12:43:02.347480Z",
     "iopub.status.busy": "2021-06-01T12:43:02.347480Z",
     "iopub.status.idle": "2021-06-01T12:43:02.356484Z",
     "shell.execute_reply": "2021-06-01T12:43:02.353482Z",
     "shell.execute_reply.started": "2021-06-01T12:43:02.347480Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "'D:\\\\Code\\\\PYTHON\\\\pycharm\\\\运筹学大作业\\\\notebooks\\\\题目四-航班机型分配'"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cwd = os.getcwd()\n",
    "cwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-01T12:43:02.366482Z",
     "iopub.status.busy": "2021-06-01T12:43:02.365482Z",
     "iopub.status.idle": "2021-06-01T12:43:02.388484Z",
     "shell.execute_reply": "2021-06-01T12:43:02.385483Z",
     "shell.execute_reply.started": "2021-06-01T12:43:02.366482Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "'D:\\\\Code\\\\PYTHON\\\\pycharm\\\\运筹学大作业\\\\notebooks\\\\题目四-航班机型分配\\\\数据'"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dir = os.path.join(cwd,\"数据\")\n",
    "data_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-01T12:45:32.194983Z",
     "iopub.status.busy": "2021-06-01T12:45:32.192984Z",
     "iopub.status.idle": "2021-06-01T12:45:32.218984Z",
     "shell.execute_reply": "2021-06-01T12:45:32.215987Z",
     "shell.execute_reply.started": "2021-06-01T12:45:32.194983Z"
    }
   },
   "outputs": [],
   "source": [
    "data_files = [os.path.join(data_dir,file) for file in os.listdir(data_dir) if os.path.splitext(file)[1] in ['.xlsx','.csv']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-01T12:54:55.650534Z",
     "iopub.status.busy": "2021-06-01T12:54:55.650534Z",
     "iopub.status.idle": "2021-06-01T12:55:19.362774Z",
     "shell.execute_reply": "2021-06-01T12:55:19.360774Z",
     "shell.execute_reply.started": "2021-06-01T12:54:55.650534Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "  Origin Destination       Flight1       Flight2 Flight3 Class   Fare  \\\n0    QWS         TFB  AA0231QWSTFB           NaN     NaN     K  178.0   \n1    QWS         TFB  AA0231QWSTFB           NaN     NaN     T  141.0   \n2    QWS         TFB  AA0231QWSTFB           NaN     NaN     G  176.0   \n3    QWS         TFB  AA0231QWSTFB           NaN     NaN     L  214.0   \n4    QWS         TFB  AA0259QWSMBY  AA0469MBYTFB     NaN     K  178.0   \n\n    deptime   arrtime  delta_time  Transfer times    TRD  Unnamed: 12  \\\n0  17:15:00  22:35:00         440               1  19.21          NaN   \n1  17:15:00  22:35:00         440               1  11.28          NaN   \n2  17:15:00  22:35:00         440               1   7.69          NaN   \n3  17:15:00  22:35:00         440               1   6.67          NaN   \n4  16:05:00  11:08:00        1263               2   4.86          NaN   \n\n  Unnamed: 13  Unnamed: 14  Unnamed: 15  Unnamed: 16  Unnamed: 17 Unnamed: 18  \\\n0         NaN          NaN          NaN          NaN          NaN         NaN   \n1         NaN          NaN          NaN          NaN          NaN         NaN   \n2         NaN          NaN          NaN          NaN          NaN         NaN   \n3         NaN          NaN          NaN          NaN          NaN         NaN   \n4         NaN          NaN          NaN          NaN          NaN         NaN   \n\n  Unnamed: 19  \n0         NaN  \n1         NaN  \n2         NaN  \n3         NaN  \n4         NaN  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Origin</th>\n      <th>Destination</th>\n      <th>Flight1</th>\n      <th>Flight2</th>\n      <th>Flight3</th>\n      <th>Class</th>\n      <th>Fare</th>\n      <th>deptime</th>\n      <th>arrtime</th>\n      <th>delta_time</th>\n      <th>Transfer times</th>\n      <th>TRD</th>\n      <th>Unnamed: 12</th>\n      <th>Unnamed: 13</th>\n      <th>Unnamed: 14</th>\n      <th>Unnamed: 15</th>\n      <th>Unnamed: 16</th>\n      <th>Unnamed: 17</th>\n      <th>Unnamed: 18</th>\n      <th>Unnamed: 19</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>QWS</td>\n      <td>TFB</td>\n      <td>AA0231QWSTFB</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>K</td>\n      <td>178.0</td>\n      <td>17:15:00</td>\n      <td>22:35:00</td>\n      <td>440</td>\n      <td>1</td>\n      <td>19.21</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>QWS</td>\n      <td>TFB</td>\n      <td>AA0231QWSTFB</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>T</td>\n      <td>141.0</td>\n      <td>17:15:00</td>\n      <td>22:35:00</td>\n      <td>440</td>\n      <td>1</td>\n      <td>11.28</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>QWS</td>\n      <td>TFB</td>\n      <td>AA0231QWSTFB</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>G</td>\n      <td>176.0</td>\n      <td>17:15:00</td>\n      <td>22:35:00</td>\n      <td>440</td>\n      <td>1</td>\n      <td>7.69</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>QWS</td>\n      <td>TFB</td>\n      <td>AA0231QWSTFB</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>L</td>\n      <td>214.0</td>\n      <td>17:15:00</td>\n      <td>22:35:00</td>\n      <td>440</td>\n      <td>1</td>\n      <td>6.67</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>QWS</td>\n      <td>TFB</td>\n      <td>AA0259QWSMBY</td>\n      <td>AA0469MBYTFB</td>\n      <td>NaN</td>\n      <td>K</td>\n      <td>178.0</td>\n      <td>16:05:00</td>\n      <td>11:08:00</td>\n      <td>1263</td>\n      <td>2</td>\n      <td>4.86</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(data_files[-1],sheet_name='TOTAL')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-01T12:58:46.361553Z",
     "iopub.status.busy": "2021-06-01T12:58:46.359538Z",
     "iopub.status.idle": "2021-06-01T12:58:46.391540Z",
     "shell.execute_reply": "2021-06-01T12:58:46.388544Z",
     "shell.execute_reply.started": "2021-06-01T12:58:46.361553Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "  Origin Destination       Flight1       Flight2 Flight3 Class   Fare  \\\n0    QWS         TFB  AA0231QWSTFB           NaN     NaN     K  178.0   \n1    QWS         TFB  AA0231QWSTFB           NaN     NaN     T  141.0   \n2    QWS         TFB  AA0231QWSTFB           NaN     NaN     G  176.0   \n3    QWS         TFB  AA0231QWSTFB           NaN     NaN     L  214.0   \n4    QWS         TFB  AA0259QWSMBY  AA0469MBYTFB     NaN     K  178.0   \n\n    deptime   arrtime  delta_time  Transfer times    TRD  \n0  17:15:00  22:35:00         440               1  19.21  \n1  17:15:00  22:35:00         440               1  11.28  \n2  17:15:00  22:35:00         440               1   7.69  \n3  17:15:00  22:35:00         440               1   6.67  \n4  16:05:00  11:08:00        1263               2   4.86  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Origin</th>\n      <th>Destination</th>\n      <th>Flight1</th>\n      <th>Flight2</th>\n      <th>Flight3</th>\n      <th>Class</th>\n      <th>Fare</th>\n      <th>deptime</th>\n      <th>arrtime</th>\n      <th>delta_time</th>\n      <th>Transfer times</th>\n      <th>TRD</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>QWS</td>\n      <td>TFB</td>\n      <td>AA0231QWSTFB</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>K</td>\n      <td>178.0</td>\n      <td>17:15:00</td>\n      <td>22:35:00</td>\n      <td>440</td>\n      <td>1</td>\n      <td>19.21</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>QWS</td>\n      <td>TFB</td>\n      <td>AA0231QWSTFB</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>T</td>\n      <td>141.0</td>\n      <td>17:15:00</td>\n      <td>22:35:00</td>\n      <td>440</td>\n      <td>1</td>\n      <td>11.28</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>QWS</td>\n      <td>TFB</td>\n      <td>AA0231QWSTFB</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>G</td>\n      <td>176.0</td>\n      <td>17:15:00</td>\n      <td>22:35:00</td>\n      <td>440</td>\n      <td>1</td>\n      <td>7.69</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>QWS</td>\n      <td>TFB</td>\n      <td>AA0231QWSTFB</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>L</td>\n      <td>214.0</td>\n      <td>17:15:00</td>\n      <td>22:35:00</td>\n      <td>440</td>\n      <td>1</td>\n      <td>6.67</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>QWS</td>\n      <td>TFB</td>\n      <td>AA0259QWSMBY</td>\n      <td>AA0469MBYTFB</td>\n      <td>NaN</td>\n      <td>K</td>\n      <td>178.0</td>\n      <td>16:05:00</td>\n      <td>11:08:00</td>\n      <td>1263</td>\n      <td>2</td>\n      <td>4.86</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(columns=df.columns[12:],inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-01T12:59:15.847115Z",
     "iopub.status.busy": "2021-06-01T12:59:15.846115Z",
     "iopub.status.idle": "2021-06-01T12:59:15.877145Z",
     "shell.execute_reply": "2021-06-01T12:59:15.874144Z",
     "shell.execute_reply.started": "2021-06-01T12:59:15.847115Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "count    47190.000000\nmean         1.738200\nstd          7.129937\nmin          0.000000\n25%          0.000000\n50%          0.000000\n75%          0.640000\nmax        158.150000\nName: TRD, dtype: float64"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.TRD.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'module' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mTypeError\u001B[0m                                 Traceback (most recent call last)",
      "\u001B[1;32m<ipython-input-8-ae3ea4278925>\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[1;32m----> 1\u001B[1;33m \u001B[0mss\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mstats\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mdf\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mTRD\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m      2\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;31mTypeError\u001B[0m: 'module' object is not callable"
     ]
    }
   ],
   "source": [
    "ss.norm(0.5).pdf"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "X = ss.norm(1,0.5)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "(array(1.), array(0.25))"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.stats()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "x = df.TRD.unique()\n",
    "x = np.sort(x)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [
    "y = [len(df.loc[df.TRD <= i,:]) for i in x]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "47190"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[-1]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x18c93dfbd30>]"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.step(x,y)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [],
   "source": [
    "y = np.array(y) / max(y)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x18c93798640>]"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.step(x,y)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "data": {
      "text/plain": "(array([611., 175.,  62.,  29.,   7.]),\n array([ 20.02 ,  47.646,  75.272, 102.898, 130.524, 158.15 ]),\n <BarContainer object of 5 artists>)"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df.TRD[df.TRD>20],5)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "data": {
      "text/plain": "0.575058275058275"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}